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9082 



Bureau of Mines Information Circular/1986 



Mineral Consumption Forecasting 
Standardizing and Comparing 
Forecasts 



By John B. Bennett 



UNITED STATES DEPARTMENT OF THE INTERIOR 



^Ti^^i^H 



Information Circular 9082 



Mineral Consumption Forecasting 
Standardizing and Comparing 
Forecasts 



By John B. Bennett 







UNITED STATES DEPARTMENT OF THE INTERIOR 
Donald Paul Hodel, Secretary 

BUREAU OF MINES 
Robert C. Horton, Director 



As the Nation's principal conservation agency, the Department of the Interior has 
responsibility for most of our nationally owned public lands and natural resources. This 
includes fostering the wisest use of our land and water resources, protecting our fish and 
wildlife, preserving the environment and cultural values of our national parks and 
historical places, and providing for the enjoyment of life through outdoor recreation. The 
Department assesses our energy and mineral resources and works to assure that their 
development is in the best interests of all our people. The Department also has a major 
responsibility for American Indian reservation communities and for people who live in 
island territories under ILS. administration. 






no** 




Library of Congress Cataloging in Publication Data 



Bennett, John B. 

Mineral consumption forecasting 

(Information circular; 9082) 

Bibliography: p. 11 

Supt. of Docs, no.: I 28.27: 9082. 

1. Mineral industries -Forecasting -Mathematical models. I. Title. II. Series: Informa- 
tion circular (United States. Bureau of Mines); 9082. 

TN295.U4 [HD9506.A2] 622 s [333.8'513] 86-600082 



For sale by the Superintendent of Documents, U.S. Government Printing Office 
Washington, DC 20402 



CONTENTS 

Paere 

Abstract 1 

Introduction 2 

Forecast standardization and comparison ,. 2 

Problems in comparing mineral consumption forecasts 2 

A standardization methodology and examples 4 

GNP growth assumptions 10 

Concluding statement 10 

References 11 

Appendix A. -Review of forecasting methodologies 12 

Appendix B.- Implied consumption divided by actual consumption, 1980^83 17 



ILLUSTRATIONS 

1. Rebased consumption projections for 2000 divided by 1980 actual consumption: United States and world 7 

2. Rebased 1980-83 implied consumption divided by 1980-83 actual consumption: United States and world 9 



TABLES 

1. Mineral consumption forecasts for the year 2000 3 

2. Projected consumption in 2000 3 

3. Standardization example 4 

4. Mineral consumption 1970-83 4 

5. Original base 5 

6. Adjusted base 5 

7. Original base divided by adjusted base 6 

8. 1980 consumption and rebased projected consumption in 2000 6 

9. Rebased projected consumption in 2000 divided by 1980 actual consumption 6 

10. Comparison example: Trend versus actual 8 

11. Growth rates used to compute implied values of consumption for 1980-83 8 

12. 1980-83 implied consumption divided by actual consumption 9 



UNIT OF MEASURE ABBREVIATIONS USED IN THIS REPORT 



kmt 


thousand metric tons 


lb 


pound 


MM lb 


million pounds 



Mmt 


million metric tons 


Mst 


thousand short tons 


mt 


metric ton 



Mineral Consumption Forecasting: 
Standardizing and Comparing Forecasts 

By John B. Bennett 1 



ABSTRACT 

This Bureau of Mines report presents a method of standardizing forecasts of mineral 
consumption that attempts to resolve the problems caused by the use of different data 
bases, different definitions of minerals consumption, and different base years for making 
projections. Using this method, forecasts of U.S. and world mineral consumption, in the 
year 2000, for nine commodities (aluminum, chromium, cobalt, copper, manganese, 
nickel, tin, tungsten, and zinc) are standardized and compared. Also selected forecasts or 
associated growth rates are used to generate implied values of consumption for 1 §80-83, 
which are then compared to actual values of consumption for 1980-83. The economic 
growth predictions underlying these forecasts are examined, and some general conclu- 
sions are drawn. A description of each methodology used to generate these forecasts is 
given in an appendix. 

'Economist, Branch of Economic Analysis, Bureau of Mines, Washington, DC. 



INTRODUCTION 



The Bureau of Mines develops forecasts of the future 
consumption of minerals and materials to aid in identifying 
and anticipating changes that might affect the national in- 
terest. Anticipating changes in the level and distribution of 
mineral demand is important for formulating long-run 
policies pertaining to the adequacy of mineral supply, for 
planning investment in U.S. mining facilities, for forming 
trade and development policies, and for planning the 
modernization and growth of rich and poor nations alike. 
The Bureau forecasts, made on both a national and a global 
basis, assist the Secretary of the Interior in carrying out his 
responsibilities under the Strategic and Critical Materials 
Stock Piling Act, which directs him to investigate the pro- 
duction and utilization of minerals and materials, and also 
assist him in helping to ensure the continued strength of the 
domestic mineral and material economy and the main- 
tenance of an adequate mineral and material base. Fore- 
casts of future consumption are published every 5 years in 
detail in Mineral Facts and Problems and, on an interim 
basis as necessary, in Mineral Commodity Profiles. The 
Bureau forecasts provide high, low, and probable levels of 
mineral and material consumption in the United States and 
the rest of the world as a whole. The Bureau also forecasts 
U.S. consumption by end use. 

The comparison of forecasts from different sources, ac- 
companied by a knowledge of how those forecasts were 
generated, offers the advantage of insight into how others 



view the world, and the possibility of learning from those 
views. The comparison of forecasts, however, frequently is 
hindered by differences in definitions, use of different base 
years, and other factors. 

In this study, a method of standardizing forecasts of 
mineral consumption is presented that attempts to resolve 
the problems caused by the use of different data bases, dif- 
ferent definitions of minerals consumption, and different 
base years for making projections. These problems have 
previously remained unresolved, or have been addressed in 
a patchwork fashion, in earlier works that presented 
forecasts of different forecasters (3). 2 Using the method 
described here, forecasts for nine minerals for the year 2000 
are standardized and compared, for the United States and 
the world. The forecasts were for consumption of the follow- 
ing commodities: aluminum, chromium, cobalt, copper, 
manganese, nickel, tin, tungsten, and zinc. The forecasts ex- 
amined in some detail are from publications by Fischman 
(.4), Leontief (9), Malenbaum (11), Ridker (13), and the 
Bureau of Mines (14). Also selected forecasts or associated 
growth rates are used to generate implied values of con- 
sumption for 1980-83, which are then compared to actual 
values of consumption for 1980-83. The economic growth 
predictions underlying these forecasts are then examined, 
and some general conclusions are drawn. A description of 
each methodology used to generate these forecasts is given 
in appendix A. 



FORECAST STANDARDIZATION AND COMPARISON 



PROBLEMS IN COMPARING MINERAL 
CONSUMPTION FORECASTS 

In comparing forecasts of mineral consumption from 
various sources, several problems arise. First, the definition 
of consumption may vary. The consumption of a particular 
commodity may be defined, for example, as apparent con- 
sumption, primary consumption, primary plus secondary 
consumption, or industrial consumption. Sometimes the dis- 
tinctions between such definitions are made clear when, data 
are presented, sometimes not. Moreover, even when the 
same definition of consumption is used, the underlying data 
series used may not be the same. There are various sources 
of mineral consumption data, and over time, revisions, up- 
dates, and changes in definitions occur, not necessarily at 
the same time, for the different sources. Revisions of data 
series, in particular, occasionally go back a number of years. 
Also, the various data series may reflect different degrees 
of processing -for example, ore series are used occasionally 
by some forecasters. Third, when forecasts are made at dif- 
ferent points in time, the data base will cover a different 
time span, which might lead to different evaluations of like- 
ly trends. 

The data in table 1 illustrate these problems. These 
mineral consumption forecasts were gathered from the 
works reviewed in appendix A. The forecasts were made at 
approximately the same time; indeed one of the reasons 
they were picked was to avoid as much as possible the prob- 
lem of forecasts made when world conditions are quite dif- 
ferent. The Fischman, Ridker, and Bureau of Mines projec- 
tions were published in 1980, Malenbaum's in 1977, and 
Leontief s in 1983. The forecasts are of U.S. and world con- 
sumption in the year 2000, for the following commodities: 



aluminum, chromium, cobalt, copper, manganese, nickel, 
tin, tungsten, and zinc. They are given in their original units 
in table 1- short tons, metric tons, and pounds. In table 2, 
the forecasts are all converted to the same unit, thousand 
metric tons. The procedures used for converting the 
forecasts are given in the table notes. 

Of the five sets of forecasts, four different definitions of 
consumption are used: apparent, primary, gross domestic 
demand, and total consumption. Also, while both Fischman 
and Malenbaum use the apparent consumption concept, 
Fischman's manganese and chromium projections are in 
terms of metal, while Malenbaum's are in terms of the 
respective ores. The ore figures were converted to refined 
metal using the same ratios used in the Global 2000 (3) study 
for this purpose. Mineral analysts could disagree with these 
ore conversion ratios, which serves to illustrate the prob- 
lem. Since the forecasts are for similar but not identical 
items, numerical comparisons of the forecasts with each 
other are strained. 

Comparisons of forecasts to currently available data are 
subject to the same kinds of problems. A series of forecasts 
from various sources, compared to current data, might offer 
useful information to decision makers or to those currently 
at work on their own projections. After all, many decisions 
are made on the basis of forecasts, and new forecasts are 
generated relatively often. One might argue that a forecast 
for a future time period, for example the year 2000, cannot 
be evaluated until that year arrives. At that point its ac- 
curacy could easily be determined. However, such forecasts 



italicized numbers in parentheses refer to items in the list of references 
preceding appendix A. 



do have implications for the intervening years. In 1999, to 
take an extreme example, one should be able to evaluate 
whether or not consumption in the following year will ap- 
proximate a given forecast for 2000. In a more general 
sense, these forecasts, in conjunction with the base period 
used to generate them, have implications for the general 



path consumption will travel on the way to 2000. If a 
forecaster, in say 1980, expects consumption of a given 
mineral to decline by 2000, and instead it increases every 
year in the 1981-85 period, one might properly doubt the ac- 
curacy of the forecast of decline. Of course, quite a different 
evaluation could result some years down the road. 



Table 1.— Mineral consumption forecasts for the year 2000 

(Data in original units) 



Commodity and area Fischman'(^) Leontief 2 (9) Malenbaum 3 (11) Ridker 4 (73) BuMines*(74) 

Aluminum: 

U.S 11.1 Mmt 14.3 Mmt 13,073.0 kmt 16,493.0 Mst 17,200.0 Mst 

World 46.2 Mmt ND 36,516.0 kmt 60,965.0 Mst 63,700.0 Mst 

Chromium: 

U.S ND 1,150.0 kmt 1,601.0 kmt 1,107.0 kmt 1,240.0 Mst 

World 6.7 Mmt 8,220.0 kmt 16,018.0 kmt 2,016.0 Mst 7,950.0 Mst 

Cobalt: 

U.S 33.5 MMIb ND 16,608.0 mt 19.0 Mst 40 MMIb 

World 96.0 MMIb ND 57,532.0 mt 123.0 Mst 107 MMIb 

Copper: 

U.S 3.1 Mmt 4.3 Mmt 3,202.0 kmt 3,265.0 Mst 4,600.0 kmt 

World 16.3 Mmt ND 16,839.0 kmt 16,418.0 Mst 23,600.0 kmt 

Manganese: 

U.S ND 2,390.0 kmt 4,002.0 kmt 2,458.0 Mst 2,000.0 Mst 

World 17.1 Mmt 35,600.0 kmt 48,060.0 kmt 10,765.0 Mst 19,600.0 Mst 

N icKsl* 

U.S ND 355.8 kmt 280.1 kmt 525.0 Mst 600.0 Mst 

World ND 1,314.1 kmt 2,122.0 Mst 2,500.0 Mst 

Tin: 

U.S ND 113.0 kmt 67.0 kmt 114.0 Mst 65,000.0 kmt 

World ND 727.0 kmt 393.0 kmt 582.0 Mst 313,800.0 kmt 

Tungsten: 

U.S ND 17.4 kmt 12,006.0 mt 15.0 Mst 61 MMIb 

World ND 125.0 kmt 92,637.0 mt 69.0 Mst 219 MMIb 

Zinc: 

U.S 1.5 Mmt 3.5 Mmt 2,001.0 kmt 2,616.0 Mst 1,800.0 kmt 

World 10.7 Mmt ND 12,022.0 kmt 12,819.0 Mst 10,600.0 kmt 

ND No data. 

1 Apparent consumption. 

2 Primary consumption; technological change assumed. 

3 Apparent consumption; Cr and Mn as ore, primary Al. 

4 Gross domestic demand tor U.S. data; total consumption, primary and secondary, for world data; base scenario for U.S. data; standard case for 
world data. 

s Probable total consumption, primary and secondary, for both U.S. and world data. 



Table 2.— Projected consumption in 2000 

(All data converted to thousand metric tons) 1 

Commodity and area Fischman 2 (4) Leontief 3 (9) Malenbaum" (11) Ridker*(73) BuMines 6 (74) 

Aluminum: 

United States 11,100.0 14,340.0 13,073.0 14,962.4 15,603.8 

World 46,200.0 ND 36,516.0 55,307.4 57,788.6 

Chromium: 

United States ND 1,150.0 438.7 1,004.3 1,124.9 

World 6,670.0 8,220.0 4,388.9 1,828.9 7,212.2 

Cobalt: 

United States 15.2 ND 16.6 17.2 18.1 

World 43.5 ND 57.5 111.6 48.5 

Copper: 

United States 3,100.0 4,290.0 3,202.0 2,962.0 4,600.0 

World 16,300.0 ND 16,839.0 14,894.4 23,600.0 

Manganese: 

United States ND 2,390.0 1,921.0 2,229.9 1,814.4 

World 17,100.0 35,600.0 23,068.8 9,766.0 17,781.1 

Nickel: 

United States ND 355.8 280.1 476.3 544.3 

World ND ND 1,314.1 1,925.1 2,268.0 

Tin: 

United States ND 113.0 67.0 103.4 65.0 

World ND 727.0 393.0 528.0 313.8 

Tungsten: 

United States ND 17.4 12.0 13.6 27.7 

World ND 125.0 92.6 62.6 99.3 

Zinc: 

United States 1,470.0 3,520.0 2,001.0 2,373.2 1,800.0 

World 10,730.0 ND 12,022.0 11,629.4 1 0,600.0 

ND No data. 

' Short tons multiplied by 0.9072, metric tons divided by 1,000, million metric tons multiplied by 1,000, pounds converted to short tons (divided by 
2,000), manganese ore multiplied by 0.48, chrome ore multiplied by 0.274. 

2 Apparent consumption. 

3 Primary consumption; technological change assumed. 

4 Apparent consumption; Cr and Mn as ore, primary Al. 

5 Gross domestic demand for U.S. data; total consumption, primary and secondary, for world data; base scenario for U.S. data; standard case for 
world data. 

6 Probable total consumption, primary and secondary, for both U.S. and world data. 



A STANDARDIZATION METHODOLOGY AND 
EXAMPLES 

The problem in mineral consumption forecast com- 
parison is identical to the familiar apples and oranges prob- 
lem. How do you compare values when they measure dif- 
ferent things? The problem would be eliminated if the 
forecasts could be recast into the same units or into a unit- 
free form. The following standardization procedure was 
developed to accomplish both these ends. The procedure is 
demonstrated first with a hypothetical example and then 
with data ^'•awn from the various works reviewed in appen- 
dix A. 

In the hypothetical example given in table 3, it is as- 
sumed that there are two forecasters, forecaster X and 
forecaster Y, using the base years 1975 and 1980, respec- 
tively. Forecaster X projects the level of consumption in the 
year 2000 to be 2,500 units, while forecaster Y projects that 
level to be 3,000 units. In other words, forecaster X expects 
consumption of this commodity to be 2V2 times its 1975 level 
in the year 2000, while forecaster Y expects consumption of 
the commodity, in the year 2000, to be twice its 1980 level. 
Suppose that another set of data is available, covering both 
the years 1975 and 1980. For the purpose of illustration, 
this latter set of data will be called the basic data. Suppose 



Table 3. 


—Standardization Example 




Base Year Projection (2000) 


Data Source 


1975 1980 Original Rebased 


Forecaster X 


1,000 NAp 2,500 3,000 


Forecaster Y 


NAp 1,500 3,000 3,200 

1,200 1,600 NAp NAp 



that both forecasters X and Y had used the basic data values 
for 1975 (1,200) and 1980 (1,600), respectively, instead of 
their original values, in making their projections. Then, 
assuming they still projected the same rate of increase be- 
tween their base years and the year 2000, the new or re- 
based projections would be 3,000 and 3,200 units, respec- 
tively. Now if the basic data (1,200 and 1,600) are in the 
same units, the new projections will also be in the same 
units, and comparisons can be readily made. 

This procedure was carried out on data collected from the 
works reviewed in appendix A. First, the base data from the 
various works were collected. None of the studies reviewed 
used the same base data set, even those published at the 
same time. The various bases used included a 1971-75 
average, a 1975-77 average, 1978, 1972, 1970, and 1971. All 
of the base year data, with the exception of Ridker's, who 
used 1971, were published in the s works reviewed. Each 
forecast for the year 2000 was then divided by the base year 
figure. The resulting ratios showed the amount of increase 
between the base year and the year 2000. 

Next, a basic table of mineral consumption was con- 
structed, for the United States and the world, using the 
latest data available at the time of writing. This basje table 
included values for each of the nine commodities listed in 
table 1 over the period 1970-83; values of world chromium 
and manganese production are used in this table to repre- 
sent consumption data for these minerals, as no world con- 
sumption data were available. The period 1970-83 was used 
because it included every base year used by the various 
forecasters considered in this study. These data are 
presented in table 4. From this basic table, an adjusted ver- 
sion of the base data used by the various forecasters was 
compiled. A new value for each base year (or average) used 



Table 4.— Mineral consumption, 1970-83, thousand metric tons 

Year and area Al Cr' Co Cu Mn 1 Ni Sn W Zn 

1970: 

United States 3,871.0 498.1 7.3 1,883.0 1,203.9 202.7 66.8 8.2 1,235.0 

World 12,160.1 1,868.8 22.1 7,283.7 8,204.7 566.6 227.0 ND 5,055.9 

1971: 

United States 4,347.3 366.5 6.2 1,880.0 1,061.4 180.2 62.1 6.7 1,199.0 

World 12,936.9 1,981.3 18.4 7,349.7 9,070.2 516.2 235.2 31.8 5,172.3 

1972: 

United States 5,025.9 508.0 8.8 2,185.0 1,239.2 213.5 61.2 7.1 1,377.0 

World 14,156.8 1,981.3 26.5 7,984.7 9,082.9 573.7 235.2 35.5 5,709.4 

1973: 

United States 5,941.0 545.2 10.0 2,208.0 1,409.8 239.4 65.1 9.9 1,474.0 

World 16,359.9 1,999.5 29.9 8,445.9 9,707.0 652.3 253.3 39.2 6,283.0 

1974: 

United States 5,625.0 567.0 10.9 2,210.0 1,353.5 256.8 72.6 10.7 1,311.0 

World 16,705.9 2,207.2 29.2 8,390.8 9,253.4 703.8 243.9 37.4 5,998.1 

1975: 

United States 4,079.0 372.0 6.4 1,467.0 1,027.9 198.9 53.7 6.3 1,331.0 

World 13,891.5 2,540.2 21.8 7,457.5 9,797.8 576.2 218.0 32.7 5,092.4 

1976: 

United States 5,196.0 479.9 9.1 1,946.0 1,237.4 220.5 57.9 7.8 1,298.0 

World 16,857.8 2,669.0 24.5 8,538.7 9,979.2 670.3 239.1 36.1 5,764.4 

1977: 

United States 5,649.0 517.1 8.2 2,045.0 1,381.7 231.3 58.3 8.5 1,154.0 

World 17,563.3 2,883.1 24.5 9,056.4 8,709.1 642.2 231.2 42.2 5,808.4 

1978: 

United States 6,111.0 535.2 9.1 2,369.0 1,236.5 247.4 58.9 10.1 1,154.0 

World 18,569.0 2,820.5 25.9 9,530.4 8,618.4 697.4 231.5 48.6 6,209.3 

1979: 

United States 6,030.0 553.4 8.6 2,432.0 1,134.0 204.8 53.5 10.8 932.0 

World 19,452.8 2,935.7 24.5 9,829.8 9,797.8 750.2 236.8 51.2 6,323.8 

1980: 

United States 5,223.0 532.5 7.7 2,175.0 933.5 186.7 46.7 9.9 951.0 

World 18,773.0 2,972.9 22.7 9,361.3 9,707.0 715.3 234.6 49.1 6,124.3 

1981: 

United States 5,209.0 462.7 5.9 2,278.0 931.7 186.9 52.5 10.3 1,146.0 

World 18,216.5 2,786.0 18.6 9,508.0 8,437.0 663.0 225.6 47.0 5,994.4 

1982: 

United States 4,811.0 289.4 5.0 1,761.0 609.6 163.7 30.3 6.1 869.0 

World 17,823.5 2,489.4 16.3 9,067.6 8,709.1 651.8 215.4 40.0 5,916.9 

1983: 

United States 5,442.0 298.5 7.3 2,020.0 606.0 185.4 38.1 6.5 1,005.0 

World 19,358.6 2,490.3 20.9 9,113.2 7,983.4 683.0 215.2 41.1 6,132.0 

ND No data. ' World production data used to represent world consumption data. 

Sources: U.S. Bureau of Mines, Mineral Facts and Problems, 1980 and 1985 Editions, for all data except as follows: Cobalt data from William Kirk, 
commodity specialist, Bureau of Mines; world data for Al, Cu, Ni, Sn, and Zn from World Metal Statistics; world data for W from Tungsten Statistics. 



by each forecaster was chosen (or computed). For example, 
if the forecaster used 1972 values as the base of his U.S. 
predictions, as did Leontief, new values for 1972 were taken 
from the basic table to form an adjusted base for Leontief. 
Thus, the values of the base year used by each forecaster 
were cast into the same unite, from the same table. The 
original and adjusted data bases are shown in tables 5 and 6, 
respectively. Table 7 shows the ratios of the values of table 5 
to those of table 6. These ratios clearly show the differences 
in the underlying data bases and demonstrate the need for 
such an adjustment. 

Values from the new, "adjusted" data base were then 
used, along with the ratio of each forecast for 2000 to the 
original base, to recompute the 2000 predictions. This was 
done for each forecaster, except Ridker, since the tetter's 



base data were not known. Although the base years of the 
various forecasts in this recomputed form were still dif- 
ferent, all predictions were now based on data in the same 
unite coming from the same table. These recomputed or 
rebased values are given in table 8. The recomputed values 
were then divided by the actual values of 1980 consumption 
from table 4. The resulting ratios provide a unit-free method 
of comparing the expected change between 1980 and 2000 
among the various forecasters. Since these ratios determine 
growth rates between 1980 and 2000, this method can also 
be used to standardize growth rates- i.e., the effective 
growth rates for various forecasters between a common 
year (in this case 1980) and the year 2000 can be deter- 
mined. The ratios are given in table 9 and are displayed 
graphically in figure 1. 



Table 5.— Original base, thousand metric tons 

Commodity and area Fischman ' (4) Leontief 2 (9) 

Aluminum: 

United States 5,480.0 4,800 

World 16,050.0 ND 

Chromium: 

United States ND 460.5 

World 3,680.0 2,100 

Cobalt: 

United States 9.1 ND 

World 26.8 ND 

Copper: 

United States 1,890.0 1,760 

World 8,370.0 ND 

Manganese: 

United States ND 1,236.8 

World .' 9,000.0 8,240 

Nickel 

United States ND 152 

World ND ND 

Tin: 

United States ND 49.85 

World ND 222 

Tungsten: 

United States ND 6.45 

World ND 33.6 

Zinc: 

United States 1,080.0 1,310 

World 5,750.0 ND 

ND No data. ' Average 1973-77. 2 1972 U.S. data, 1970 world data. 3 Average 1971-75. 



Malenbaum 3 (77) 



BuMines"(74) 



4,388.1 


6,111.8 


12,248.7 


18,270.1 


314.8 


498.96 


1,901.8 


3,538.1 


7.282 


9.18 


23.434 


24.66 


1,886.1 


2,380 


7,922.7 


10,100 


926 


1,236.5 


9,762.7 


8,692.8 


160.5 


217.4 


618.4 


881.8 


52.6 


53.9 


232.5 


281 


6.51 


10.21 


40.18 


47.29 


1,159.8 


1,229 


5,506.26 


6,779 



1978. 



Table 6.— Adjusted base, thousand metric tons 

Commodity and area Fischman ' (4) Leontief 2 (9) 

Aluminum: 

United States 5,298.0 5,025.9 

World 16,275.7 ND 

Chromium: 

United States ND 508.0 

World 2,459.8 1,868.8 

Cobalt: 

United States 8.9 ND 

World 26.0 ND 

Copper: 

United States 1,975.2 2,185.0 

World 8,377.9 ND 

Manganese: 

United States ND 1,239.2 

World 9,489.3 8,204.7 

Nickel: 

United States , ND 213.5 

World ND ND 

Tin: 

United States ND 61.2 

World ND 227.0 

Tungsten: 

United States ND 7.1 

World ND ND 

Zinc: 

United States 1,313.6 1,377.0 

World 5,789.3 ND 

ND No data. ' Average 1973-77. 2 1972 U.S. data, 1970 world data. 3 Average 1971-75. 



Malenbaum 3 (77) 



BuMines 4 (74) 



5,003.6 
14,810.2 


6,111.8 
18,569.5 


471.7 
2,141.9 


535.2 
2,820.5 


8.4 
25.2 


9.1 
25.9 


1,990.0 
7,925.7 


2,369.0 
9,530.4 


1,218.4 
9,382.3 


1,236.5 
8,618.4 


217.8 
604.5 


247.4 
697.4 


62.9 
235.8 


58.9 
231.5 


8.2 

35.3 


10.1 
48.6 


1,338.4 
5,651.0 


1,154.0 
6,209.3 



1978. 



Table 7.— Original base divided by adjusted base 

Commodity and area Fischman (4) Leontief (9) 

Aluminum: 

United States 1.03 0.96 

World .99 ND 

Chromium: 

United States ND .91 

World 1.50 1.12 

Cobalt: 

United States 1.02 ND 

World 1.03 ND 

Copper: 

United States .96 .81 

World 1.00 ND 

Manganese: 

United States ND 1.00 

World .95 1.00 

Nickel: 

United States ND .71 

World ND ND 

Tin: 

United States ND .81 

World ND .98 

Tungsten: 

Urifted States ND .91 

World ND ND 

Zinc: 

U.S .82 .95 

World .99 ND 

ND No data. 



Malenbaum (11) 


BuMines (14) 


0.88 
.83 


1.00 
.98 


.67 

.89 


.93 
1.25 


.87 

.93 


1.01 
.95 


.95 

1.00 


1.00 
1.06 


.76 

1.04 


1.00 
1.01 


.74 
1.02 


.88 
1.26 


.84 
.99 


.92 

1.21 


.79 
1.14 


1.01 
.97 


.87 
.97 


1.06 
1.09 



Table 8.— 1980 consumption and rebased projected consumption In 2000, 

Commodity and area Actual 1980' Fischman (4) Leontief (9) 

Aluminum: 

United States 5,223.0 10,731.4 15,552.6 

World 18,773.0 46,849.7 ND 

Chromium: 

United States 532.5 ND 1,268.6 

World 2,972.9 4,458.4 7,315.0 

Cobalt: 

United States 7.7 14.9 ND 

World 22.7 42.2 ND 

Copper: 

United States 2,175.0 3,239.7 5,325.9 

World 9,361.3 16,315.4 ND 

Manganese: 

United States 933.5 ND 2,394.6 

World 9,707.0 18,029.7 35,447.5 

Nickel: 

United States 186.7 ND 499.8 

World 715.3 ND ND 

Tin: 

United States 46.7 ND 138.7 

World 234.6 ND 743.4 

Tungsten: 

United States 9.9 ND 19.2 

World 49.1 ND ND 

Zinc: 

United States 951.0 1,788.0 3,700.0 

World 6,124.3 10,803.3 ND 

ND No data. ' Data from Mineral Facts and Problems, 1985. 



thousand metric tons 



Malenbaum (11) 



BuMines (14) 



14,906.7 
44,152.4 


15,601.8 
58,735.6 


657.4 
4,943.0 


1,206.7 
5,749.4 


19.1 
61.8 


17.9 

50.9 


3,378.4 
16,845.4 


4,578.7 
22,269.1 


2,527.6 
22,169.9 


1,814.4 
17,628.9 


380.1 
1,284.6 


619.4 
1,793.7 


80.1 
398.6 


71.0 
258.5 


15.1 
81.4 


27.5 
102.1 


2,309.1 
12,338.0 


1,690.2 
9,709.2 



Table 9.— Rebased projected consumption In 2000 divided by 1980 actual consumption 

Commodity and area Fischman (4) Leontief (9) Malenbaum (11) 

Aluminum: 

United States 2.05 2.98 2.85 

World 2.50 ND 2.35 

Chromium: 

United States ND 2.38 1.23 

World 1.50 2.46 1.66 

Cobalt: 

United States 1.93 ND 2.48 

World 1.86 ND 2.73 

Copper: 

United States 1.49 2.45 1.55 

World, 1.74 ND 1.80 

Manganese: 

United States ND 2.57 2.71 

World 1.86 3.65 2.28 

Nickel: 

United States ND 2.68 2.04 

World ND ND 1.80 

Tin: 

United States ND 2.97 1.72 

World ND 3.17 1.70 

Tungsten: 

United States ND 1.93 1.52 

World ND ND 1.66 

Zinc: 

United States 1.88 3.89 2.43 

World 176 ND 2.01 

ND No data. 



BuMines (14) 



2.99 
3.13 



2.27 
1.93 



2.32 
2.24 



2.11 
2.38 



1.94 
1.82 



3.32 
2.51 



1.52 
1.10 



2.77 
2.08 



1.78 
1.59 



5 2 

CO 



UNITED STATES 



/ 
/ 
/ 
/ 
/ 
/ 
/ 




& 




KEY 

Fischman (4) 



r~]Leontief (9) 



Malenbaum [11) 
BuMines [14) 



Al 



Cr 



Co 



Cu 



Mn 



Ni Sn 



Zn 




KEY 
Fischman (4) 



f~ni_eontief (9) 



Malenbaum [11) 
BuMines [14) 



Al Cr Co Cu Mn Ni Sn W Zn 

Figure 1.— Rebased consumption projections for 2000 divided by 1980 actual consumption: United States and world. 



The related problem of comparing forecasts to currently 
available data was approached in the following manner. If 
the base year and base data are given, along with the fore- 
cast, growth rates of consumption can be computed. Such 
growth rates are in fact commonly included in studies con- 
taining forecasts. With these growth rates, and the base 
year data, values of consumption can be computed for the 
interval of years between the base year and forecast year. 
These values would not be expected to be equal to actual 
values every year, or even any year, of the forecast period, 
since mineral consumption fluctuates from year to year, 
depending on, among other things, the business cycle. Still 
these values, when accumulated over a period of years, 
should give an idea of the cumulative consumption the fore- 
caster expected in that period. This sum would also give an 
idea, when compared to actual values, of how well that 
forecast seemed to fit the real world at some point in time. 
Of course, the accuracy of a given forecast, as judged by this 
method, would vary with the period chosen as the evalua- 
tion period. 

A hypothetical example of this approach is given in table 
10. Forecaster X (of table 3) projected a 2V2 times increase 
in consumption between 1975 and 2000. This rate of in- 
crease implies a growth rate of 3.7%. Using the base year of 
forecaster X (1975) and the value of that year (1,200) from 
the basic data series of table 3, values of consumption for 
1980, 1981, 1982, and 1983 were determined; these values 
are simply those values that result in the years 1980, 1981, 
1982, and 1983 from a growth of 3.7% beginning in 1975 at 
a value of 1,200. Accompanying those values in table 10 are 
"actual" values, meant to represent actual mineral consump- 



Table 10.— Comparison example: Trend versus actual with 
forecaster X's growth rate (3.7%) and new base (1,200 units in 

1975) 





Year 


Forecaster X 
trend values 


"Actual" 
values 


1980 

1981 

1982 

1983 




1,440 

1,488 

1,548 

1,608 


1,200 
1,400 
1,200 
1,800 


Total . 


6,084 


5,600 



tion, which does not grow along a smooth trend, but rather 
fluctuates. These "actual" values are also assumed to come 
from the basic data series and therefore are in the same 
units as the computed values. The sums of the two sets of 
1980-83 values are shown; in this case, the sum of the "im- 
plied" values is fairly close to the sum of the "actual" values. 

This approach was carried out on the data gathered in 
this study in the following manner. First, growth rates that 
included the period 1980-83 were gathered from the various 
studies, if they were available. If they were unavailable, 
they were computed. These growth rates are given in table 
11. These growth rates are not strictly comparable, since 
they cover various periods of time. Next, using these 
growth rates and the adjusted data base for each forecaster, 
"implied" values of consumption for 1980, 1981, 1982, and 
1983 were calculated. These values were summed over each 
commodity, for each forecaster, and divided by the actual 
value of consumption over that period, taken from the basic 
table (table 4). The resulting ratios, given in table 12 and 
displayed graphically in figure 2, were computed from 
growth rates projected -or computed using projections -by 
Fischman, Leontief, Malenbaum, Ridker, and the Bureau. 
Appendix B contains similar ratios calculated on a year-by- 
year basis. Although Ridker did not publish base year data, 
he did publish growth rates for the consumption of various 
minerals in the United States for 1971-85. These growth 
rates, along with values for 1971 consumption from table 4, 
were used to calculate "implied" values of consumption for 
1980-83. 

From the ratios presented in table 9 and figure 1, it is 
clear that quite a range exists for estimates of mineral con- 
sumption in 2000, at least for some minerals. Also, the 
ratios presented in table 12 and figure 2 show that the sum 
of the "implied" values of the forecasts or associated growth 
rates also followed the sum of the actual values of consump- 
tion for the period 1980-83 with varying degrees of ac- 
curacy, as one would expect given the range shown in table 
9. In table 12 those ratios close to 1 indicate "implied" values 
quite close, in aggregate, to actual values over that period. 
As mentioned earlier, this kind of evaluation depends on the 
period of time chosen, and might show quite different 
results in a later time period. 



Table 11.— Growth rates used to compute implied values of consumption for 1980-83 

Commodity and area Fischman'(4) Leontief 2 (9) Malenbaum 3 (11) Ridker 4 (73) BuMines 5 (74) 

Aluminum: 

United States 4.0 4.0 4.3 4.83 4.4 

World 5.3 ND 4.2 ND 5.4 

Chromium: 

United States ND 3.3 1.3 3.01 3.4 

World 2.7 5.0 3.2 ND 3.3 

Cobalt: 

United States 3.0 ND 3.2 3.41 2.9 

World 2.3 ND 3.6 ND 3.0 

Copper: 

United States 1.9 3.2 2.6 2.15 3.0 

World 3.1 ND 2.9 ND 3.9 

Manganese: 

United States ND 2.4 3.4 2.37 1.4 

World 2.6 5.3 3.2 ND 2.7 

NicKsl' 

United States ND 3.1 2.2 3.17 4.0 

World ND ND 3.1 ND 4.3 

Tin: 

United States ND 3.0 .9 2.16 .9 

World ND 4.2 2.1 ND .9 

Tungsten: 

United States ND 3.6 2.1 3.90 4.6 

World ND 4.7 3.3 ND 3.5 

Zinc: 

United States .7 3.6 2.6 2.36 1.7 

World 2^5 ND 3J3 ND 2J 

ND No data. 'Computed; U.S. and world 1975-85. 'Computed; U.S. 1972-2000; world 1970-90. 3 Published; U.S. and world 
1975-85. " Published; U.S. 1975-85. 5 Published; U.S. and world 1978-2000. 



Table 12. 

Commodity and area 

Aluminum: 

United States 

World 

Chromium: 

United States 

World 

Cobalt: 

United States 

World 

Copper: 

United States 

World 

Manganese: 

United States 

World 

Nickel: 

United States 

World 

Tin: 

United States 

World 

Tungsten: 

United States 

World 

Zinc: 

U.S 

World 

ND No data. 



-1980-83 Implied consumption divided by actual consumption 

(Adjusted data base of each forecaster) 



Fischman (4) 



Leontief (9) 


Malenbaum (77) 


1.41 
ND 


1.27 
1.04 


1.75 
1.22 


1.29 
.98 


ND 
ND 


1.60 
1.62 


1.44 
ND 


1.14 
1.03 


2.01 
1.71 


1.97 
1.33 


1.58 
ND 


1.39 
1.09 


1.93- 
1.63 


1.58 
1.21 


1.21 
ND 


1.15 
.98 


1.94 
ND 


1.59 
1.15 



Ridker (73) 



BuMines (14) 



1.32 
1.23 

ND 
1.09 

1.67 
1.53 

1.08 
1.10 

ND 
1.29 

ND 
ND 

ND 
ND 

ND 
ND 

1.38 
1.13 



1.38 
ND 

1.27 
ND 

1.37 
ND 

1.14 
ND 

1.76 
ND 

1.38 
ND 

1.85 
ND 

1.23 
ND 

1.54 
ND 



1.38 
1.21 

1.52 
1.18 

1.55 
1.46 

1.28 
1.18 

1.69 
1.11 

1.51 
1.20 

1.45 
1.08 

1.46 
1.24 

1.23 
1.11 




KEY 

ischman (4) 



□ F1 

^Leontief (9) 



Malenbaum (71) 

V^ Ridker 
i^S Watson (73) 

HI BuMines (74) 



KEY 



j J Fischman (4) 
Leontief (9) 



Malenbaum (17) 
BuMines (74) 



Figure 



Al Cr Co Cu Mn Ni Sn W Zn 

2.— Rebased 1980-83 implied consumption divided by 1980-83 actual consumption: United States and world. 



10 



GNP GROWTH ASSUMPTIONS 

With the differences in forecasts clearly identified and 
measured, those interested in understanding the reasons for 
the differences can delve deeper into the various procedures 
and underlying assumptions. A description of the various 
methodologies used to generate the forecasts examined in 
the study is given in appendix A. These procedures are of 
varying degrees of complexity, with emphasis on different 
exogenous variables and relationships among variables. A 
thorough comparison of these procedures would involve 
assessing the assumptions behind each model, examining 
whether the procedures follow logically from the assump- 
tions, determining if the outputs could be replicated, and 
assessing the costs and benefits of each model (1, p. 307). 
The costs include the initial cost of development, the cost of 
maintaining the model, and operating costs; benefits include 
the improvement in forecast accuracy, the level of con- 
fidence provided by the model, and the ability to assess 
alternative scenarios of the future. An extensive com- 
parison of the procedures along these lines was beyond the 
scope of this study. 

One assumption, however, the assumed rate of growth 
of gross national product (GNP) or gross domestic product 
(GDP), was compared because every forecast examined was 
based on some assumption about GNP or GDP growth. 
Also, most of the forecasting studies reviewed did make ex- 
plicit their assumptions about growth in U.S. output, and 
some did so concerning growth in world output. Finally, the 
actual values of GNP and GDP growth in the period 1980-83 
were readily available. 

In the period 1980-83 economic growth in both the 
United States and the world was quite low. Real U.S. GNP 
grew at the rate of -0.3%, 2.5%, -2.1%, and 3.7% for 
1980, 1981, 1982, and 1983, respectively (5, p. 205). World 
output grew at 2.0%, 1.6%, 0.6%, and 2.6% over the same 
years (5, p. 205). These rates, on average, were much lower 
than those assumed by the various forecasters, whose 
assumptions, of course, covered longer periods of time. (As 
the period 1980-83 seems to have been a cyclical low for 
business, in the longer run, their assumptions will probably 
turn out to be much more accurate.) Malenbaum (11, p. 38), 
for example, assumed a growth in U.S. GDP of 3.3% and 
world GDP of 3.5% over the period 1975-85. Fischman (U, p. 
142) assumed a growth rate of U.S. GNP of 3.0% over the 
period 1980-85. Leontief (9, p. 33) assumed a 3.1% increase 



in U.S. GNP over the period 1972-2000. Ridker (13, p. 141) 
assumed a 2.9% increase in U.S. GNP over the period 
1971-85 and over 3.5% rates of growth in the rest of the 
world. The Bureau of Mines 3 in its 1980 statistical estimates 
of U.S. consumption in 2000, assumed a 3.0% growth rate 
of U.S. GNP between 1978 and 2000. Overall the various 
forecasters made quite similar assumptions about GNP 
growth rates. 

The methodology presented in the previous section 
resulted in ratios that when close to 1 indicate "implied" 
values quite close, in aggregate, to actual values over that 
period. Looking at table 12 in particular, what is striking is 
that almost all of the ratios are greater than 1. Thus most of 
the "implied" forecasts exceeded the actual value of con- 
sumption of these minerals during the period 1980-83. 

Given the actual low values of GNP and GDP growth in 
the 1980-83 period, it is not too surprising that so many of 
the values in table 12 exceed 1. What may be surprising is 
that a number of projections resulted in ratios fairly close to 
1, say within 10%, under the assumption of growth rates in 
economic output that were much higher than were actually 
experienced during this period. Since mineral consumption 
generally varies directly with economic output, one can 
readily hypothesize that if these forecasters had assumed 
lower values of economic growth rates, their forecasts of 
mineral consumption would have been lower, everything 
else remaining the same, and the implied consumption 
derived in the previous section would have been smaller. 
Again their estimates of GNP were for longer periods of 
time, and the 1980-83 period seems to have been a cyclical 
low for business. If lower growth rates had been used, 
however, ratios of the type found in table 12 might then 
have been far less than 1. In other words, the forecasters 
whose ratios were close to 1 in table 12 might have under- 
estimated mineral consumption, if they had used in their 
procedures the growth rates that actually prevailed during 
the 1980-83 period! 

Were these forecasters assuming more substitution for 
minerals, for instance, than actually took place? Or were 
they assuming more technological change, or more of a shift 
in the composition of GNP toward the service sector than 
actually occurred? What do these questions, and the possible 
answers, imply about projections currently being for- 
mulated? These and other questions arise from an analysis 
of this kind and illustrate its usefulness. 



CONCLUDING STATEMENT 



The comparison of mineral consumption forecasts from 
various sources involves numerous difficulties. The stand- 
ardization procedure presented in this study provides a way, 
given adequate data, to overcome some of these difficulties. 
Standardized forecasts can be compared, in a precise 
fashion, to other standardized forecasts, and used for 
various purposes. For example, a set of standardized 
forecasts or standardized growth rates can be used to con- 
struct various scenarios of future events. Standardized 
forecasts can also be compared to available current data, us- 
ing the growth rates associated with the forecasts to 
venerate "implied" values for various years. The latter kind 



of comparison does not, however, provide a way of 
evaluating the underlying forecasting methodologies. In 
such an evaluation, assessments should be made of the pro- 
cedures or models and of the predictions of the important 
causal variables within the models. In the case of mineral 
consumption forecasts, GNP predictions should be ex- 
amined. The kind of examination of past forecasts for future 
time periods presented in this paper, along with a look at the 
underlying GNP predictions, was thought to be useful for 
decision makers and forecasters working with current data. 
Such an examination could help them adjust or confirm their 
own forecasts for future time periods. 



11 



REFERENCES 



1. Armstrong, J.S. Long-Range Forecasting. Wiley, 1985, 
689 pp. 

2. Cammarota, V. A. Jr., W. Y. Mo, and B. W. Klein. Projections 
and Forecasts of U.S. Mineral Demand by the U.S. Bureau of 
Mines. Pres. at "109th Annual Meeting, AIME (Feb. 24-29, 1980, 
Las Vegas, NV), 4 pp.; available upon request from V.A. Cam- 
marota, Jr., BuMines, Washington, D.C. 

3. Council on Environmental Quality and Department of State. 
The Global 2000 Report to the President. Volume 2: The Technical 
Report. 1980, p. 582 (table 22-1). 

4. Fischman, L. L. World Mineral Trends and U.S. Supply Prob- 
lems. Resources For The Future, Inc., Res. Paper R-20, Oct. 1980, 
535 pp. 

5. International Monetary Fund. World Economic Outlook. 
Washington, DC, Apr. 1985, 283 pp. 

6. Lansberg^JI. H. Natural Resources for U.S. Growth. Johns 
Hopkins Press, 1964, 260 pp. 

7. Lansberg, H. H., L. L. Fischman, and J. L. Fisher. Resources 
in America's Future. Johns Hopkins Press, 1963, 1040 pp. 



8. Leontief, W., A. P. Carter, and P. A. Petri. The Future of the 
World Economy. Oxford Press, 1977, 110 pp. 

9. Leontief, W., J. CM. Koo, S. Nasar, and I. Sohn. The Future of 
Nonfuel Minerals in the U.S. and World Economy. NY Univ., 1983, 
512 pp. 

10. Malenbaum, W. Materials Requirements in the United States 
and Abroad in the Year 2000. Natl. Comm. on Mater. Policy, 
Washington, DC, 1973, 40 pp. 

11. Malenbaum, W. World Demand for Raw Materials in 1985 
and 2000. Natl. Sci. Found., NS/RA-770421, Oct. 1977, 153 pp. 

12. Meadows, D., D. Meadows, J. Randers, and W. W. Behrens. 
The Limits To Growth. Universe Books, 1974, 205 pp. 

13. Ridker, R. G., and W. D. Watson. To Choose a Future. John 
Hopkins Press, 1980, 463 pp. 

14. U.S. Bureau of Mines. Mineral Facts and Problems, 1980 Edi- 
tion. BuMines B 671, 1981, 1060 pp. 



12 



APPENDIX A.- REVIEW OF FORECASTING METHODOLOGIES 



RESOURCE PROJECTIONS 

Long-term projections of the U.S. demand for nonfuel 
minerals have been made for over 20 years. In the pioneer 
1963 book Resources in America's Future (7), Landsberg 
projected for 1980 and 2000 both fuel and nonfuel minerals 
requirements, along with those for land, lumber, water, 
chemicals, and labor. The study compared the projected 
resource requirements with the 1960 U.S. resource base. 
These results were also presented in a condensed version of 
the original study entitled Natural Resources for U.S. 
Growth (6). 

The starting point for these demand projections was a 
series of projections of population, labor force, and gross na- 
tional product. Requirements for food, clothing, shelter, 
heat and power, transportation, durable goods, military 
equipment, outdoor recreation, etc. were then calculated. 
Next, these goods and services were translated into re- 
quirements for resource products such as agricultural raw 
materials, steel, lumber, and textile fibers. From these, in 
turn, the various demands on land, water, fuels, and other 
resources were estimated. Nearly all projections were made 
at three levels -low, medium, and high -with the middle 
levels considered most likely. (The low projections for 1980, 
in most cases, were closest to the actual 1980 levels of con- 
sumption.) 

Long-term projections of resource needs on a global 
basis have been made for over 10 years. With the publica- 
tion of Limits to Growth;(12) in 1974, long-term global pro- 
jections, already a subject of some debate, became con- 
troversial. In this book it was argued that the limits to 
growth on the planet would be reached sometime within the 
next century, if present trends continued. It was also 
argued that, given present resource consumption rates and 
the projected increase in these rates, most important 
nonrenewable resources would be extremely costly in the 
future (12, p. 66). 

These conclusions were reached with the use of a 
simplistic world model that featured one general population, 
one geographic unit, one composite industrial output, one 
nonrenewable resource, and one class of pollutants. Within 
this highly aggregated model, the basic behavior mode of 
the world system was asserted to be exponential growth of 
populatirn and capital, followed by collapse. Technological 
change had no impact on the essential problem, exponential 
growth in a finite and complex system (12, p. 145). Since the 
publication of Limits to Growth, arguments have been ad- 
vanced both for and against the notion that man's progress 
would be limited in the future by resource scarcity. 

STATISTICAL AND CONTINGENCY 
FORECASTING 

In 1970 the Bureau of Mines began making mineral con- 
sumption projections for the United States and the rest of 
the world. These forecasts are presented in editions of 
Mineral Facts and Problems, which is published every 5 
years. For the United States, two kinds of consumption 
forecasts are developed: statistical projections and con- 
tingency forecasts (2). 

The statistical projections are calculated by the Division 
of Minerals Policy and Analysis, using linear regression 
analyses. Simple linear regression equations are used to ap- 



proximate the end-use consumption of mineral com- 
modities, where macroeconomic variables are selected as 
possible explanatory variables. For each end use of a par- 
ticular mineral, a set of likely explanatory variables is 
chosen from among 75 possibilities (gross national product 
(GNP), gross private domestic investment (GPDI), U.S. 
population, new construction activity, and 71 different 
Federal Reserve Board industrial production indexes). Each 
variable within the chosen set is then used as the ex- 
planatory variable in a series of simple linear regressions 
over some historical period, usually starting in 1960, with 
the dependent variable being the end use of the material. 
From the equations so generated, the one with the highest 
R 2 (smallest sum of squared predicted errors) is chosen as 
the basis for deriving the statistical projection. The coeffi- 
cients of this equation, plus a value of the explanatory 
variable projected to the year 2000 by a selected economic 
forecaster outside the Bureau, is then used to generate a 
projection for the end use. 

The contingency forecasts are formulated by commodi- 
ty specialists. These forecasts are judgmental and are based 
both on historical trends and on the specialists' knowledge 
of all developments, current and anticipated, that might 
have an impact on the future use of a commodity. In making 
these forecasts, the commodity specialists identify those 
problems or opportunities that might cause the consumption 
of a particular commodity to deviate from its historical 
trend. Using the statistical projections as a guide, the 
specialist arrives at estimates of low, high, and most prob- 
able consumption growth. Other specialists then critique 
these estimates, using, among other things, their knowledge 
of how the growth in consumption of the commodity in ques- 
tion might be affected by the growth of consumption of 
other commodities. All of the contingency forecasts utilize 
the statistical projections as their point of departure and 
assume the same economic conditions that underlie the 
statistical projections. 

Individual commodity specialists also make the world 
consumption forecasts. For these forecasts, reliance is 
placed on trends in world economic growth, population 
growth, and the growth in consumption of selected minerals 
and materials. Also commodity specialists have access to 
forecasts made by knowledgeable consultants, mining com- 
panies, international commodity associations, and other 
economic groups. 

INTENSITY OF USE 

In a March 1973 study (10) for the U.S. Commission on 
Materials Policy, Professor Wilfred Malenbaum of the 
University of Pennsylvania's Wharton School of Finance 
and Commerce prepared projections of mineral demand by 
a methodology known as intensity of use. In this study, 
trends in overall economic growth, population growth, and 
growth in primary consumption of 11 minerals and 
materials were projected for the world, and for 10 world 
regions. The U.S. National Commission on Materials Policy 
was responsible for examining the feasibility of striking a 
balance between the national need to produce goods on the 
one hand and to protect the environment on the other. In 
October 1977 (11), following the publication of Limits to 
Growth and the oil price shocks brought on by the Organiza- 
tion of Petroleum Exporting Countries (OPEC), Malenbaum 



13 



updated his 1973 study at the request of the National 
Science Foundation. The 1977 report dropped some of his 
original commodities, added others, and revised downward 
the projections for consumption of aluminum, copper, iron, 
steel, and zinc. 

Underlying the Malenbaum reports is the premise that 
long-term growth is not governed by supply limitations of 
any specific input materials. He argued that economic ex- 
pansion is based on the human resources of the world's 
societies (11, p. 48), and that the aspirations and commit- 
ment to economic expansion on the part of public and 
private leaders mattered more than resource endowment 
and technology (11, p. 26). Thus, the direction of determina- 
tion would run from gross domestic product (GDP) to 
material use. This assumption made it possible to project na- 
tional and world economic growth in one part of the 
research effort without regard to the material needs ap- 
praised in the other part of the study. 

Malenbaum thought that the record of growth in 
various parts of the world in the early 1970's lent support to 
the decisive role of quality inputs, both in rich lands where 
growth was impressive and in poor lands where the 
economic performance was more uncertain (11, p. 26). His 
appraisals of this situation, especially within the third 
world, led to his lower estimates of average GDP growth 
rates in the 1977 study than in the earlier study. In the 1977 
study, he forecast the world in 2000 as having per capita 
GDP some 50% above 1971-75 levels in real terms, and as 
using two to three times the volume of raw materials in a 
year compared with average annual usage over 5 recent 
years. These results contrasted markedly with the conclu- 
sions of the 1973 study, where comparable ratios were 
reported between three and four. Thus, his expectation in 
1977 was for a relative weakening of demand, and lower 
relative mineral prices. It was not a picture of a world con- 
fronted with limited resources (11, p. 122). 

In brief, intensity of use (IOU) analysis is a procedure 
for translating GDP and population projections into mineral 
and material consumption projections, using tables that 
estimate the intensity with which minerals or materials will 
be consumed within a given country or region relative to per 
capita GDP levels. As used by Malenbaum, this analysis con- 
siders only primary use and disregards subsequent 
shipments of processed or manufactured minerals or 
materials to other regions. Hence, Japan, for example, is 
represented as having exceptionally high consumption 
levels, since Japanese exports are disregarded. Total world 
consumption of a given mineral or material is calculated as 
the sum of the consumption levels for the commodity pro- 
jected for each region. 

The following country groupings were used by Malen- 
baum: 

1. Western Europe -Organization for Economic 
Cooperation and Development (OECD) countries in 
Europe. 

2. Japan. 

3. Other developed lands -Australia, Canada, Israel, 
New Zealand, Republic of South Africa. 

4. U.S.S.R. 

5. Eastern European Countries -Soviet bloc countries 
plus Albania and Yugoslavia. 

6. Africa minus South Africa. 

7. Asia minus Israel, Japan, China, Mongolia, North 
Korea and North Vietnam. 

8. Latin America. 

9. China plus Mongolia, North Korea and North Vietnam. 
10. United States plus Puerto Rico and overseas islands. 



Each of the 10 groups was considered to have a high 
enough degree of homogeneity to justify common assump- 
tions with respect to changes in IOU of raw materials and in 
rates of total economic growth. Calculations regarding 
future mineral consumption levels are independent of 
similar calculations involving that region's consumption of 
other minerals and materials, and independent of any other 
region's consumption levels of any commodity. They are 
also independent of any explicit considerations regarding 
potential changes in supply levels, prices, or strategic or 
balance of payment positions. 

Consumption of a given mineral or material within a 
given year is calculated on the basis of just three com- 
ponents: an exogenous projection of the level of overall 
economic activity (GDP) within a given region in a given 
year, an exogenous projection of the total population within 
the same region in the same year, and an "IOU table" show- 
ing the quantity of a given mineral or material per unit of 
that region's total GDP (a ratio known as the commodity's 
intensity of use) likely to be consumed within that region at 
various levels of regional per capita GDP. From the IOU 
table, the appropriate IOU value is obtained (expressed in 
terms of commodity units per unit of total GDP) for the 
regional per capita GDP level in question, interpolating or 
extrapolating as necessary. This IOU value is then 
multiplied by the exogenously estimated total regional GDP 
for that year. 

On analysis of the historical record, Malenbaum found 
what seemed to be patterns strong enough to allow IOU 
projections. He thought that these patterns had a 
technological dimension, reflecting changes in use and effi- 
ciency of inputs to outputs, both taking account of changes 
in techniques (for input or output) and changes in market 
relationships associated with supply, demand, and public 
policy. Also he thought the analytic and descriptive 
literature on material use provided some guides for projec- 
ting the appropriate intensity levels. Primarily, however, it 
was the apparently systematic behavior of the measure that 
underlay his conviction of its usefulness in demand analysis 
for raw materials (11, p. 22). 

The primary pattern found was formed by the IOU in a 
region and its per capita GDP. The IOU statistic increased 
as a function of increasing per capita GDP for less 
developed countries, and decreased for industrialized coun- 
tries moving toward postindustrial service economies. Thus, 
mineral and metal consumption levels within a region whose 
economy is moving from industrialization to postin- 
dustrialization are projected using IOU statistics at various 
levels of per capita GDP that form an inverted U-shaped 
curve. For most of the materials Malenbaum considered, 
world intensity of use seemed to have already reached 
historical peak levels, mostly a decade or more back (11, p. 
49). This conclusion supported his view that for the entire 
world, a large proportion of total world use will long con- 
tinue to occur in the wealthier lands (11, p. 121). 

The materials analyzed in the 1977 study were alumi- 
num, chrome, cobalt, copper, iron ore, manganese, nickel, 
platinum, crude steel, tin, tungsten, and zinc. IOU data for 
these minerals were assembled for the same time intervals, 
1951-75, with occasional data for 1934-38, on the same 
regional bases as were GDP and population. Most of the na- 
tional product data and all of the population data were taken 
from United Nations sources. The output was converted to 
U.S. dollars in 1971 prices on the basis of exchange rate 
data. For the most part, the historical record was examined 
in the form of 5-year averages with recourse to individual 
years only to trace patterns of marked change within a 



14 



5-year period, a problem that arose especially for regions 
composed of countries not usually analyzed as a single unit 
(11, p. 25). 

The material use data were from the Bureau of Mines, 
the United Kingdom's Summary of the Mineral Industry, 
the U.N. Department of Economic and Social Affairs, and 
occasional specialized private groups associated with pro- 
ducing and processing interests (e.g., the publishers of Ger- 
many's Metal Statistics).The consumption concept was "ap- 
parent consumption" or production minus exports plus im- 
ports plus changes in stocks (11, p. 29). 



INTENSITYOF-USE VARIATION 

A variation on the IOU methodology was utilized in 
World Minerals Trends and U.S. Supply Problems (4) by 
Leonard L. Fischman of Resources For The Future. In this 
book the historical patterns of consumption for seven non- 
fuel minerals -aluminum, chromium, cobalt, copper, 
manganese, lead, and zinc -are examined. Projections of 
future patterns of consumption are offered, based for the 
most part on demographic and macroeconomic projec- 
tions -that is, growth in GDP, or GDP per capita. The prin- 
cipal conclusion of his study was that the U.S. faces only one 
important type of mineral supply problem, based on its 
dependence on imports of certain minerals. The imports of a 
few of these minerals, chromium in particular, may be sub- 
ject to disruptions that could cause a sharp upward move- 
ment of prices, with serious economic impacts (.4, p. 3). 

The general procedure for making the mineral com- 
modity projections in this study was, first, to relate con- 
sumption of the refined metal to the appropriate macro in- 
dicator, usually GNP or GDP, and then to derive the ore or 
semirefined input into the refined metal; steel production 
was used as a macro indicator in several cases, yet it in turn 
was related to GNP or GDP (.4, p. 144). Five-year moving 
averages of the ratio of consumption to GNP or GDP 
(averages of IOU ratios) were calculated for historical data 
periods of selected countries, in order to smooth out varia- 
tions due to business cycles. Projections of the moving 
averages of these ratios were made, with the intention of 
achieving a similar smoothing pattern. The precise pro- 
cedure used to make the projections was not specified, but it 
seems that the projections were judgmental. Elaborate 
statistical "filtering" methods were rejected as infeasible, 
and straight-line trends were rejected as inappropriate. 

Fischman's world totals were based on the selected in- 
dividual countries that account for the bulk of each com- 
modity's utilization. By and large, these are the leading in- 
dustrial countries for the refined metals, and a combination 
of industrial countries and other mineral-rich countries for 
the cruder forms of metals. It takes projections of the 
macroeconomies of only about a dozen countries, all told, ac- 
cording to Fischman, to provide the base for projecting the 
bulk of the consumption of all 18 commodity forms covered, 
plus crude steel. The aggregate consumption of the leading 
consumers of each form, though gradually declining in rela- 
tion to world totals as new consumers enter the picture, 
may, it was hypothesized, be "blown up" fairly accurately to 
a world total for any given future year by extrapolating 
their declining share (4, p. 136). 

Fischman argued that the difference between his macro 
assumptions and those of others should be taken as the ex- 
pression of how he judged the future would unfold. He main- 
tained that "whenever long-term commodity projections are 



based directly or indirectly on long-term projections of 
gross economic output, the commodity-projection accuracy 
tends to depend more upon the accuracy of the gross na- 
tional or domestic product (GNP or GDP) projections than 
upon the parameters tha,t join GNP/GDP to individual com- 
modities" (4, p. 3). He concluded that "prior projections of 
world mineral consumption have been almost uniformly too 
high -principally because of overestimation of long-term 
growth rates in gross economic output of the principal con- 
suming countries" (4, p. 137). 

INPUT-OUTPUT 

A quite different approach to the projecting of mineral 
requirements was used by Leontief in two studies. In The 
Future of the World Economy (8), published in 1977 with 
U.N. funding, a large and complex input-output model was 
constructed and used to, among other things, project the 
production and consumption of six nonfuel minerals. 
Overall, the model addressed the question of global resource 
requirements and the availability of food, mineral, and 
energy resources to meet these requirements to the year 
2000. Macroeconomic variables, such as gross domestic 
product, consumption, investment, and the balance of 
payments, were projected for 15 regions into which the 
world's countries were aggregated. Also projected were a 
large number of sectoral outputs, including those of 30 
manufacturing and service sectors, 4 agricultural sectors, 3 
energy resource outputs, and 6 nonfuel mineral outputs: 
aluminum, copper, iron, lead, nickel, and zinc. In addition to 
output levels, the model tracked imports and exports in 
minerals, as well as all other traded goods. For 
nonrenewable fuel and nonfuel minerals, the model traced 
the cumulative resources produced in each region after each 
decade. 

In a second book, The Future of Nonfuel Minerals in the 
U.S. and World Economy (9), the earlier study was updated 
and expanded, and the outlook for nonfuel minerals was 
stressed. Twenty-six nonfuel minerals were included in the 
second study: iron and ferrous metals (chromium, 
manganese, molybdenum, nickel, silicon, tungsten, 
vanadium), nonferrous metals (aluminum, copper, gold, 
lead, magnesium, mercury, platinum, silver, tin, titanium, 
and zinc), fertilizer-related minerals (phosphate rock, 
potash), and , miscellaneous chemicals (boron, chlorine, 
fluorine, soda ash, and sulfur). 

An input-output table shows the amounts of goods and 
services individual industries buy from and sell to each other 
in a particular year. Input coefficients are derived from such 
a table by dividing the column entries by the respective sec- 
toral outputs. The coefficients show the amount of each in- 
put required to produce one dollar's worth of a sector's out- 
put. Each column describes the structure of a particular in- 
dustry and, by giving a detailed, quantitative description of 
the inputs used by the industry, serves as an implicit 
representation of that industry's technology. Because each 
industry has its own column, the matrix is a structural 
description of the entire economy for a particular year. 
Similarly, a separate set of capital coefficients describes the 
stocks of buildings and equipment, as well as all kinds of 
working inventories, that each producing sector has to 
maintain to transform the proper combination of its inputs 
into its final output of goods and services (9, p. 20). The in- 
puts of primary natural resources, such as agricultural land, 
water, and minerals, required by all producing sectors of the 
economy as well as households can also be depicted and 



15 



analyzed. Input-output tables are prepared by the Bureau of 
Economic Analysis (BEA) every 5 years (9, p. 23). 

The world economy (in the U.N. World Input-Output 
Model) is subdivided into 15 regions that fall into 3 main 
groups: the developed regions, characterized by relatively 
high per capita income (North America, Europe, the Soviet 
Union, Oceania, South Africa, and Japan); the less- 
developed regions rich in natural resources (the Middle 
East, some of the South American countries, and some 
countries in tropical Africa); and the less-developed coun- 
tries with few resources (9, p. 210). The model describes 
each region in terms of 45 sectors of economic activity, in- 
cluding various types of agriculture, mining, manufactur- 
ing, utilities, construction, services, transportation, com- 
munication, and pollution abatement. Though each region is 
initially treated separately, the model contains linkages that 
permit its users to trace the complex interconnections of 
trade, foreign investment, loans, interest payments, and 
foreign aid. The rates of regional or world economic growth, 
as determined by the regional rates of population growth, 
technological change, and savings, will to a great extent 
determine the global long-term requirements for nonfuel 
minerals. 

Certain assumptions and projections underlie the Leon- 
tief projections. Assumptions about technological change 
and changes in recycling rates were both incorporated 
through changes in the mineral input coefficients. Changes 
in import requirements were handled in a similar fashion. 
Bureau of Mines projections of ratios of high and low levels 
of expected imports to primary demand were reconstituted 
into import coefficients, the ratio of imports to domestic 
output, so that they would be compatible with the structure 
of the model. By surveying special studies of future trends 
in material use, making extrapolations based on past trends, 
and then qualifying these crude estimates by discussions 
with experts on material use, the interindustry coefficients 
prepared for the World Model study were updated to reflect 
expected technological change. Labor and energy coeffi- 
cients were similarly updated. 

The final U.S. demand projections were based on pro- 
jections made with the Bureau of Labor Statistics (BLS) 
macroeconomic model. The BLS model takes into considera- 
tion detailed projections of demographic trends and cor- 
responding changes in the pattern of consumption, invest- 
ment, exports, imports, and labor productivity. The size of 
the U.S. economy, measured by gross domestic product 
(GDP), was projected by the BLS to grow in real terms from 
1972 to 2000 at an average annual rate of 3.1% (9, p. 23). 

INPUT-OUTPUT AND INTENSITY 
OF USE COMBINED 

A study published in 1980 used a combination of input- 
output and intensity of use methodologies in making projec- 
tions. This study, To Choose a Future (13), by Ronald G. 
Ridker and William D. Watson, used a system of models, in- 
cluding a core input-outpift model, for its U.S. projections. 
Projections for the rest of the world were based on 
intensity-of-use calculations and projections of the growth 
of per capita income. The purpose of this study was to 
analyze the resource and environmental impacts on the 
United States of alternate rates of population and economic 
growth, with attention to international and technological 
developments and energy prices. 

The system of models consisted of a number of special- 
purpose models linked to INFORUM, the University of 



Maryland's 185-sector, dynamic, macroeconomic-cum-input- 
output model of the U.S. economy, developed over a series 
of years by Clopper Almon and his students at the Universi- 
ty of Maryland (13, p. 6). This system is known as 
SEAS/RFF (Strategic Environmental Assessment 
System/Resources For The Future). The SEAS/RFF 
system develops national U.S. economic forecasts through 
2025 based on an exogenously specified set of demographic, 
macroeconomic, energy price, environmental policy, and 
resource policy assumptions. In turn, these forecasts form 
the basic economic inputs used by other models in the 
system to develop their more specialized forecasts. 
Forecasts are made at both national and regional levels. The 
model computes both dollar and physical estimates for all 
major fuels, some 20 nonfuel minerals, and 42 pollutants. 

INFORUM is a dynamic forecasting model that joins ag- 
gregate GNP forecasts to the markets in which products are 
sold. The model determines industry outputs year by year 
based on forecasts for all product markets, for capacity, and 
for the availability of labor. Most of the final demand com- 
ponents are based on econometric equations derived from 
regressions performed by Clopper Almon and his associates 
(13, p. 418) on historical time series. Past levels of personal 
consumption expenditures were regressed against levels of 
disposable income, changes in disposable income, relative 
prices, time trends, and levels of consumption from previous 
years. 

The six special-purpose models integrated with IN- 
FORUM into a common model were PRICE, which uses 
relative energy prices and price elasticities to alter energy 
demands, capital requirements, and GNP growth; 
TECHNOLOGY, which uses technological change assump- 
tions to alter current and capital account flows; INSIDE, 
which provides greater detail on industrial output; ABATE, 
which calculates costs for abating pollution and sector pur- 
chases for abatement; ENSUPPLY, which uses assump- 
tions about fuel availability and energy supply technology to 
determine energy supply and demand mixes; and 
MINERALS, which allows exogenous specification of 
stockpiles and import levels for selected minerals (13, p. 
412). Together, these seven models form the national 
economic forecasting model (FORECAST) for the 
SEAS/RFF system. Linkage among all of the models allows 
population growth, energy price effects, technology change, 
abatement, energy supply constraints, and stockpiling and 
import constraints for minerals to be reflected in 
SEAS/RFF economic forecasts. 

A number of assumptions and scenarios were used in 
the study. For instance, the study used the Census Bureau 
projection series D, E, and F for population projections. The 
E series, which was adopted as the baseline projection, 
assumes that the groups of women just entering the 
reproductive years will have a completed fertility rate of 2.1 
births per woman. The D series assumes an average of 2.5 
births per woman, reflecting a continuation of the trends of 
the last 50 years. Series F assumes 1.8 births per woman, 
reflecting a continuation of the trends of the last 5 years (13, 
p. 20). The study also assumed that the unemployment rate, 
which stood at 8.5% at the end of 1975, returned to between 
4% and 4.5% by 1980 and remained within that range 
thereafter (13, p. 25). Two productivity assumptions were 
used: (1) worker-hour productivity returned by 1980 to the 
trend line of the 1948-68 period and (2) after 1968 the long- 
term growth rate in labor productivity shifted downward by 
0.3% per year and only half of the shortfall from this new 
trend was made up by 1980 (13, p. 27). 



16 



Four different GNP projections were made for various 
world regions, using the following scenarios: high popula- 
tion and high economic growth, low population and high 
economic growth, low population and low economic growth, 
and two intermediate cases. These projections were based 
on a division of countries into four groups, each occupying a 
somewhat unique position in the world economy. The first 
group consists of the relatively developed, industrialized 
countries (OECD members, South Africa, Eastern Europe, 
and U.S.S.R.), where economic growth is largely a matter of 
maintaining growth in labor productivity and full employ- 
ment of the labor force. Projections for countries other than 
the United States in this category begin by applying 
assumptions about growth rates in GNP per capita (an ap- 
proximation for labor productivity) to the population projec- 
tions (an approximation for the labor force). The projections 
are then modified to take into account the effects of higher 
petroleum and other import prices. Projections for the 
United States are derived from runs of the SEAS/RFF 
model. The second group of countries consists of OPEC 
members. The third group comprises countries whose 
growth rates are strongly dependent on foreign trade earn- 
ings in volatile international markets; for the most part, 
these countries are the principal nonfuel mineral exporters. 
The last group of countries consists of the resource-poor 
LDC's (13, pp. 42-44). 

To solve the models, a trial value of disposable income 
was run through the system in order to determine personal 
consumption expenditures. Personal consumption expen- 
ditures were then used with estimates of public abatement 
expenditure, number of households, and interest rates to 
determine residential and public construction and other 
variables. This process continued until a GNP figure was ob- 
tained. The calculated GNP was then compared with a 
target GNP. If they differed, the level of disposable income 
was changed and the calculations began anew. Mineral de- 
mand projections for the United States were obtained by 
first estimating a value of gross domestic demand (13, p. 



104) -that is, demand excluding exports that can be 
satisfied from primary production, secondary production, or 
imports. In those cases in which the SEAS/RFF model in- 
cluded a specific sector that could be associated with a 
specific mineral- aluminum, copper, iron, lead, or zinc -the 
demand projections were derived directly from the model. 
In all other cases, information from the Bureau of Mines 
(1975) on unit requirements (and changes in unit re- 
quirements over time) for each major mineral-using sector 
was combined with projections of the output of these sec- 
tors. With the exception of the fertilizer sector (a major user 
of phosphate rock, potash, and sulfur), for which projections 
were developed by other methods, sector projections were 
derived from the model. Secondary production, estimated 
by assuming that the percentage of demand satisfied by 
recycling remained the same as in the base period (roughly, 
the 1971-74 average), was then subtracted from these gross 
demand projections. 

The mineral demand projections for the rest of the 
world were based on intensity-of-use relationships. It was 
assumed that mineral consumption per capita was a func- 
tion of GNP per capita, and that this function changed over 
time as GNP per capita increased. The assumption was 
made that the relationship for other regions approaches 
that for the United States, though it never reaches it until 
the GNP per capita of the subject country catches up to that 
of the United States. The projections were made from four 
points in time, starting with historic data for 1971 for the 
region in question. A straight line was drawn from this first 
point through a point representing the projected relation- 
ship between consumption per capita and GNP per capita 
for the United States in 1985. The relationship for the 
region in 1985 was then found by locating its 1985 GNP per 
capita on that straight-line segment. The same procedure 
was repeated using the U.S. relationship in the year 2000 
and then again in 2025. These estimates were then 
multiplied by population (13, p. 107). 



17 



APPENDIX B.— IMPLIED CONSUMPTION DIVIDED BY ACTUAL CONSUMPTION, 

1980-83 



Commodity Implied/actual consumption, relative value 

and area 1980 1981 1982 1983 Average 

ADJUSTED BU MINES DATA (14) 

Aluminum: 

United States 1.28 1.34 1.51 1.39 1.38 

World 1.10 1.19 1.29 1.25 1.21 

Chromium: 

United States 1.08 1.27 2.11 2.12 1.52 

World 1.02 1.11 1.29 1.34 1.18 

Cobalt: 

United States 1.25 1.68 2.04 1.45 1.55 

World 1.21 1.52 1.79 1.44 1.46 

Copper: 

UnitedStates 1.15 1.13 1.52 1.36 1.28 

World 1.10 1.12 1.23 1.27 1.18 

Manganese: 

UnitedStates 1.36 1.38 2.15 2.18 1.69 

World 95 1.12 1.13 1.27 1.11 

N i cKsl ' 

UnitedStates 1.38 1.43 1.71 1.56 1.51 

World 1.06 1.20 1.27 1.27 1.20 

Tin: 

UnitedStates 1.29 1.16 2.02 1.62 1.45 

World 1.01 1.06 1.12 1.13 1.08 

Tungsten: 

UnitedStates 1.12 1.13 2.01 1.97 1.46 

World 1.06 1.15 1.40 1.42 1.24 

Zinc: 

UnitedStates 1.25 1.06 1.42 1.25 1.23 

World 1.05 1.11 1.14 1.12 1.11 

ADJUSTED FISCHMAN DATA (4) 

Aluminum: 

UnitedStates 1.23 1.28 1.45 1.33 1.32 

World 1.12 1.22 1.31 1.27 1.23 

Chromium: 

UnitedStates ND ND ND ND ND 

World 94 1.03 1.19 1.22 1.09 

Cobalt: 

UnitedStates 1.34 1.80 2.19 1.56 1.67 

World 1.28 1.59 1.86 1.50 1.53 

Copper: 

UnitedStates 1.00 .97 1.28 1.13 1.08 

World 1.04 1.06 1.15 1.18 1.10 

Manganese: 

UnitedStates ND ND ND ND ND 

World 1.10 1.30 1.31 1.46 1.29 

N icksl* 

United States ND ND ND ND ND 

World ND ND ND ND ND 

Tin: 

UnitedStates ND ND ND ND ND 

World ND ND ND ND ND 

Tungsten: 

UnitedStates ND ND ND ND ND 

World ND ND ND ND ND 

Zinc: 

United States 1.42 1.19 1.58 1.38 1.38 

World 1.07 1.12 1.16 1.15 1.13 

ND No data. 



1.41 
ND 

1.75 
1.22 

ND 
ND 

1.44 
ND 

2.01 
1.71 

1.58 
ND 

1.93 
1.63 

1.21 
ND 

1.94 
ND 



Commodity Implied/actual consumption, relative value 

and area 1980 1981 1982 1983 Average 
ADJUSTED LEONTIEF DATA (9) 

Aluminum: 

UnitedStates 1.31 1.37 1.55 1.42 

World ND ND ND ND 

Chromium: 

United States 1.24 1.47 2.44 2.43 

World 11.02 1.15 1.35 1.42 

Cobalt: 

UnitedStates ND ND ND ND 

World ND ND ND ND 

Copper: 

United States 1.30 1.28 1.70 1.54 

World ND ND ND ND 

Manganese: 

United States 1.61 1.64 2.56 2.64 

World 1L41 1.71 1.75 2.01 

Nickel: 

UnitedStates 1.46 1.51 1.77 1.61 

World ND ND ND ND 

Tin: 

United States 1166 1.52 2.71 2.22 

World 1,46 1.58 1.72 1.79 

Tungsten: 

UnitedStates 95 .94 1.67 1.61 

World ND ND ND ND 

Zinc: 

UnitedStates 1.93 1.65 2.25 2.01 

World ND ND ND ND 

ADJUSTED MALENBAUM DATA (77) 

Aluminum: 

UnitedStates 1.18 1.24 1.39 1.29 1.27 

World 97 1.04 1.11 1.06 1.04 

Chromium: 

UnitedStates 94 1.10 1.78 1.75 1.29 

World 84 .93 1.08 1.11 .98 

Cobalt: 

UnitedStates 1.27 1.72 2.10 1.49 1.60 

World 1.32 1.67 1.98 1.61 1.62 

Copper: 

UnitedStates 1.03 1.01 1.36 1.21 1.14 

World 97 .99 1.07 1.10 1.03 

Manganese: 

UnitedStates 1.54 1.60 2.52 2.63 1.97 

World 1.13 1.35 1.35 1.52 1.33 

Nickel: 

UnitedStates 1.29 1.33 1.54 1.40 1.39 

World 98 1.09 1.15 1.13 1.09 

Tin: 

UnitedStates 1.40 1.26 2.20 1.77 1.58 

World 1.12 1.18 1.27 1.29 1.21 

Tungsten: 

UnitedStates 92 .90 1.57 1.49 1.15 

World 84 .91 1.10 1.12 .98 

Zinc: 

United States 1.59 1.35 1.85 1.64 1.59 

World 1.08 1.14 1.19 1.20 1.15 



ADJUSTED RIDKER DATA (73) 

Commodity Implied/actual consumption, relative value 

(United States 

data only) 1980 1981 1982 1983 Average 

Aluminum 1.27 1.34 1.52 1.41 1.38 

Chromium 90 1.07 1.76 1.76 1.27 

Cobalt 1.09 1.47 1.80 1.28 1.37 

Copper 1.05 1.02 1.35 1.20 1.14 

Manganese 1.40 1.44 2.25 2.31 1.76 

Nickel 1.27 1.31 1.55 1.41 1.38 

Tin 1.61 1.47 2.58 2.10 1.85 

Tungsten 95 .95 1.69 1.64 1.23 

Zinc 1.55 1.32 1.78 1.57 1.54 



C 67 










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